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The Wave Matrix - Excel 2007 AddinPrev TopicNext Topic
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineNow we analyze for the whole number.
We will start with Data Direction set to -Bottom of Sheet for this sample, because the newest data is at the bottom.
Everything else is set to the defaults.
The first 10 lines of the Wave Matrix are shown below.
Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 BMA Degree 5 BMA Degree 6 BMA Degree 7 BMA Degree 8 Algorithm Predictor A = 497.8033156 -1.351 -1.096 -1.058 -1.015 -1.054 -1.02 -1.026 -1.044 Wave / BMA = 0.50 Depth = 25 B = 5.88688E-05 Amp / Freq = 0.50 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Remainder Data 1 497.8033745 24.98113757 -22.02429006 -8.554287736 -2.308357316 0.498873816 1.365504645 1.988181114 2.143404467 34.10645902 530 2 497.8034333 25.79202522 -21.81376363 -8.772849267 -2.768140159 0.050466356 0.833793133 1.465047295 1.661063954 202.7489237 697 3 497.8034922 26.7707037 -21.52973847 -9.020757103 -3.317620052 -0.486582355 0.191417613 0.832178409 1.078289149 3.678616896 496 4 497.8035511 27.91802971 -21.14733041 -9.265340991 -3.916883795 -1.079116454 -0.524573924 0.124303566 0.425891089 -180.3385299 310 5 497.80361 29.23432864 -20.63953393 -9.470612891 -4.521636361 -1.690351812 -1.272952642 -0.619916448 -0.261851195 268.4389167 757 6 497.8036688 30.71890142 -19.97951231 -9.600027787 -5.086572439 -2.282524569 -2.011367966 -1.360748004 -0.949689424 -195.2521277 292 7 497.8037277 32.36978483 -19.14136889 -9.617262046 -5.56605351 -2.817492395 -2.696801423 -2.057728957 -1.601683294 -259.675122 227 8 497.8037866 34.18320249 -18.102874 -9.489547328 -5.918242091 -3.259973434 -3.289355503 -2.673226293 -2.184316466 170.9305461 658 9 497.8038454 36.15321338 -16.84705671 -9.189537615 -6.107259307 -3.57929272 -3.75415512 -3.174183508 -2.668023404 -85.63755042 403 10 497.8039043 38.2715802 -15.36253837 -8.695554746 -6.10316462 -3.749488082 -4.061180639 -3.531889786 -3.026984577 -354.5446837 137 … … … … … … … … … … … …
Notice how the BMA Degree changes with increasing wave numbers.
There's point where the BMA Degree is going up in value then steps back down between Waves 4 and 5.
This could be an indication that we are using too many waves to analyze the data.
Since the BMA Degree changes down just after wave 4, we will stop the analysis at wave 4 by changing the Wave in the Wave Matrix Settings from 8 to 4.
Below is the new Wave Matrix for the new setting.
Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 Algorithm Predictor A = 497.8033156 -1.351 -1.096 -1.058 -1.015 Wave / BMA = 0.50 Depth = 25 B = 5.88688E-05 Amp / Freq = 0.50 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Remainder Data 1 497.8033745 24.98113757 -22.02429006 -8.554287736 -2.308357316 40.10242306 530 2 497.8034333 25.79202522 -21.81376363 -8.772849267 -2.768140159 206.7592945 697 3 497.8034922 26.7707037 -21.52973847 -9.020757103 -3.317620052 5.293919712 496 4 497.8035511 27.91802971 -21.14733041 -9.265340991 -3.916883795 -181.3920256 310 5 497.80361 29.23432864 -20.63953393 -9.470612891 -4.521636361 264.5938446 757 6 497.8036688 30.71890142 -19.97951231 -9.600027787 -5.086572439 -201.8564577 292 7 497.8037277 32.36978483 -19.14136889 -9.617262046 -5.56605351 -268.8488281 227 8 497.8037866 34.18320249 -18.102874 -9.489547328 -5.918242091 159.5236744 658 9 497.8038454 36.15321338 -16.84705671 -9.189537615 -6.107259307 -98.81320517 403 10 497.8039043 38.2715802 -15.36253837 -8.695554746 -6.10316462 -368.9142268 137 … … … … … … … … The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineIdeally we would like to see the BMA Degree start negative and become more positive; with Wave 1 and Wave 4 BMA Degrees near equal and opposite.
To do this, we need to make some setting changes in the Algorithm Settings first and if needed, changes to the Wave Find Algorithm.
We will start with the 'Amplitude to Frequency Ratio', this will tell us if the waves we need are more or less dependent on Amplitude or Frequency.
Settings that are closer to 1.00 have the effect of including values with more Amplitude analysis; likewise, settings that are closer to 0.00 have the effect of including values with more Frequency analysis.
We're looking for a nice middle ground between Amplitude and Frequency.
You can tell it's achieved by looking at the BMA values.
In the previous table, we can see the BMA Degree for waves 1 and 4 are not nearly equal and opposite.
Below we have posted a few tables with changes in the Amplitude to Frequency Ratio.
Start - Amp / Freq = 0.50Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 Algorithm Predictor A = 497.8033156 -1.351 -1.096 -1.058 -1.015 Wave / BMA = 0.50 Depth = 25 B = 5.88688E-05 Amp / Freq = 0.50 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Remainder Data 1 497.8033745 24.98113757 -22.02429006 -8.554287736 -2.308357316 40.10242306 530 2 497.8034333 25.79202522 -21.81376363 -8.772849267 -2.768140159 206.7592945 697 3 497.8034922 26.7707037 -21.52973847 -9.020757103 -3.317620052 5.293919712 496 4 497.8035511 27.91802971 -21.14733041 -9.265340991 -3.916883795 -181.3920256 310 5 497.80361 29.23432864 -20.63953393 -9.470612891 -4.521636361 264.5938446 757 6 497.8036688 30.71890142 -19.97951231 -9.600027787 -5.086572439 -201.8564577 292 7 497.8037277 32.36978483 -19.14136889 -9.617262046 -5.56605351 -268.8488281 227 8 497.8037866 34.18320249 -18.102874 -9.489547328 -5.918242091 159.5236744 658 9 497.8038454 36.15321338 -16.84705671 -9.189537615 -6.107259307 -98.81320517 403 10 497.8039043 38.2715802 -15.36253837 -8.695554746 -6.10316462 -368.9142268 137 … … … … … … … … Next - Amp /Freq = 0.60
Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 Algorithm Predictor A = 497.8033156 -0.712 -0.335 -0.384 0.04 Wave / BMA = 0.50 Depth = 25 B = 5.88688E-05 Amp / Freq = 0.60 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Remainder Data 1 497.8033745 7.330244344 -0.217893412 9.50681814 16.21656276 -0.639106308 530 2 497.8034333 6.987040545 -2.93518143 7.571375074 12.82854189 174.7447906 697 3 497.8034922 6.620527433 -6.545533103 5.016955438 8.017382107 -14.91282409 496 4 497.8035511 6.347124192 -10.67865241 2.097244016 2.734102748 -188.3033696 310 5 497.80361 6.295591584 -14.93538918 -0.919871154 -2.053064716 270.8091235 757 6 497.8036688 6.590570174 -18.99850017 -3.833975639 -5.986146542 -183.5756166 292 7 497.8037277 7.357285696 -22.51895998 -6.426241688 -8.096525036 -241.1192867 227 8 497.8037866 8.699609276 -25.30479464 -8.596463787 -8.292717918 193.6905805 658 9 497.8038454 10.693866 -27.34416439 -10.37641858 -7.573903986 -60.20322448 403 10 497.8039043 13.40026685 -28.63279818 -11.79844538 -6.663696613 -327.109231 137
Next - Amp / Freq = 0.70Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 Algorithm Predictor A = 497.8033156 -0.074 0.364 0.41 0.459 Wave / BMA = 0.50 Depth = 25 B = 5.88688E-05 Amp / Freq = 0.70 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Remainder Data 1 497.8033745 15.35353123 23.61354988 15.17873393 9.143525358 -31.09271487 530 2 497.8034333 11.36764212 17.7400217 11.64581636 7.02226772 151.4208188 697 3 497.8034922 5.89405704 8.23576343 5.228074044 2.235480627 -23.39686736 496 4 497.8035511 -0.202514768 -2.35515828 -1.733318137 -2.918185295 -180.5943746 310 5 497.80361 -6.00126102 -11.65765661 -7.206363567 -6.619324797 290.680996 757 6 497.8036688 -10.91647042 -20.02232381 -12.43336612 -11.06103809 -151.3704704 292 7 497.8037277 -14.0922454 -23.62533614 -13.19955873 -11.35476761 -208.5318198 227 8 497.8037866 -15.22658292 -21.67180234 -8.586632289 -6.282590309 211.9638213 658 9 497.8038454 -14.68871648 -17.49753499 -2.314394707 0.077347951 -60.3805472 403 10 497.8039043 -12.7706862 -12.03856652 5.184282176 8.108711115 -349.2876449 137 A tweak more - Amp / Freq = 0.65
Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 Algorithm Predictor A = 497.8033156 -0.393 0.011 0.015 0.116 Wave / BMA = 0.50 Depth = 25 B = 5.88688E-05 Amp / Freq = 0.65 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Remainder Data 1 497.8033745 8.539655945 13.65546693 13.51070963 9.955357905 -13.46456489 530 2 497.8034333 6.660219391 9.29314809 10.57640229 7.370997872 165.295799 697 3 497.8034922 4.247873242 3.131362614 6.427533445 3.551553225 -19.16181474 496 4 497.8035511 1.63250273 -3.836472384 1.88897967 -0.505098346 -186.9834628 310 5 497.80361 -0.82693352 -10.57590116 -2.197376158 -3.803119736 276.5997206 757 6 497.8036688 -2.826930658 -16.57460468 -5.520079917 -6.187834365 -174.6942192 292 7 497.8037277 -4.046001437 -20.82128434 -7.234287701 -6.433433534 -232.2687207 227 8 497.8037866 -4.281176533 -23.09225519 -7.260544542 -4.428163756 199.2583535 658 9 497.8038454 -3.467079072 -24.13669941 -6.472577633 -1.513196133 -59.21429318 403 10 497.8039043 -1.557621411 -24.50082734 -5.508205997 1.486019254 -330.7232688 137
Finally - Amp / Freq = 0.67Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 Algorithm Predictor A = 497.8033156 -0.266 0.153 0.177 0.236 Wave / BMA = 0.50 Depth = 25 B = 5.88688E-05 Amp / Freq = 0.67 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Remainder Data 1 497.8033745 10.7173074 18.15547046 14.40852069 9.391820877 -20.4764939 530 2 497.8034333 8.066673949 13.10493959 11.04349271 6.804923296 160.1765371 697 3 497.8034922 4.570171704 5.679256387 5.962851928 2.626820039 -20.64259227 496 4 497.8035511 0.717216201 -2.663525521 0.442853694 -1.833285984 -184.4668095 310 5 497.80361 -2.964227396 -10.44430749 -4.25512962 -5.323528347 282.1835829 757 6 497.8036688 -6.066503876 -17.210436 -7.994758852 -8.038826886 -166.4931432 292 7 497.8037277 -8.124682897 -21.24647608 -9.112493242 -7.944707438 -224.375368 227 8 497.8037866 -8.901038932 -22.2252807 -7.377528881 -4.70564837 203.4057103 658 9 497.8038454 -8.422716416 -21.612586 -4.454634341 -0.316182239 -59.99772643 403 10 497.8039043 -6.724736885 -20.26958689 -1.242481098 4.404521068 -336.9716205 137 At each step, we moved the Amp / Freq one way then back till we have nearly equal and opposite.
The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineIf you find that you are moving the Amp / Freq setting all the way to 1.00, then it means you are analyzing only by Amplitude.
You can help speed the Wave Matrix generation up by selecting 'by Amplitude Only'.
Also, the same is true for the other end.
If you are setting the Amp / Freq to a value of 0.00, then it means you are analyzing only by Frequency.
Selecting the 'by Frequency Only' will only run the frequency analysis.The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineIf you find that changing the Amp / Freq Ratio isn't getting the BMA values to come out equal and opposite, then you might need to reset the value to default of 0.50 and start making changes to the 'Wave to BMA RMS Ratio'.
The Wave to BMA RMS Ratio only affects the way the Amplitude analysis is done.
Settings that are closer to 0.00 tend to have more smooth wave like patterns and wave heights lower than the original data.
In addition, the closer to 0.00 the BMA values tend to change little between each BMA value.
Settings that are closer to 1.00 tend to have more wave variation, erratic, more like the original data and the wave heights are higher; similar to the original data.
Also, the the BMA values tend to become erratic as well, sometimes flip-flopping between negative and positive, or going up then back down.
When changing this setting, you are looking for a nice smooth transition between BMA values, generally from negative to positive with increasing wave numbers.The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineThere are some data sets where you're not going to get that nice setup of equal and opposite.
What you should be trying to achieve then is a relatively smooth transition between BMA degrees.
Increasing waves and/or BMA Iterations might help a bit, but each does what it can to find reasonable waves in the data set.
Adding more waves may or may not give better insight in to the possible waves need to replicate the observed data.
Adding more BMA Iterations can help with getting better waves, but it also adds more time in generating the Wave Matrix.The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineNext pick 3 example, Draw Index of a number.
The Draw Index is a relative value dependent on the number samples.
For this set we have 4809 draws and each draw has a set of numbers associated with it.
Likewise, each number has a set of Draw Indices associated with it.
Index Year Month Day A B C 1 2000 1 1 5 3 0 2 2000 1 2 6 9 7 3 2000 1 3 4 9 6 4 2000 1 4 3 1 0 5 2000 1 5 7 5 7 6 2000 1 6 2 9 2 7 2000 1 7 2 2 7 8 2000 1 8 6 5 8 9 2000 1 9 4 0 3 10 2000 1 10 1 3 7 11 2000 1 11 8 5 9 12 2000 1 12 9 8 3 13 2000 1 13 0 7 7 14 2000 1 14 4 6 6 15 2000 1 15 2 1 6 16 2000 1 16 4 2 7 17 2000 1 17 4 4 2 18 2000 1 18 8 0 7 19 2000 1 19 8 3 5 20 2000 1 20 1 2 1 21 2000 1 21 9 7 3 22 2000 1 22 0 5 2 23 2000 1 23 9 8 6 24 2000 1 24 6 6 6 25 2000 1 25 9 6 2 26 2000 1 26 5 6 6 27 2000 1 27 4 7 1 28 2000 1 28 9 0 3 29 2000 1 29 6 6 7 30 2000 1 30 6 3 4 31 2000 1 31 9 2 5 32 2000 2 1 6 2 2 33 2000 2 2 9 1 8 34 2000 2 3 1 3 6 35 2000 2 4 8 1 1 36 2000 2 5 6 5 5 37 2000 2 6 4 3 0 38 2000 2 7 4 9 4 39 2000 2 8 3 4 4 40 2000 2 9 3 8 3 41 2000 2 10 2 6 8 42 2000 2 11 1 8 5 43 2000 2 12 4 1 6 44 2000 2 13 7 6 8 45 2000 2 14 8 2 8 46 2000 2 15 7 3 9 47 2000 2 16 7 9 4 48 2000 2 17 7 3 1 49 2000 2 18 7 2 0 50 2000 2 19 6 0 3 51 2000 2 20 0 1 2 52 2000 2 21 9 6 4 53 2000 2 22 5 2 1 … … … … … … …
As an example from our draws, the number 5 in Column A occurred at Index 1, 26, 53, and so on.
The following table shows all the Draw Index for number 5 in Column A.
Number 5 Draw Index in Column A 1 996 1977 2960 3931 26 1001 1985 2973 3935 53 1003 1990 3000 3939 65 1008 2005 3020 3941 81 1013 2011 3023 3949 103 1019 2039 3025 3950 112 1023 2058 3039 3954 129 1024 2061 3043 3964 156 1025 2069 3049 3989 160 1041 2073 3056 3994 205 1054 2091 3062 4006 228 1060 2106 3065 4017 231 1061 2114 3067 4018 239 1099 2117 3106 4019 255 1112 2173 3122 4025 257 1113 2174 3123 4026 262 1120 2177 3127 4031 270 1121 2196 3143 4062 274 1175 2202 3169 4100 282 1181 2208 3172 4115 285 1191 2221 3175 4151 287 1203 2244 3176 4171 290 1213 2248 3193 4201 302 1233 2249 3197 4203 306 1236 2265 3209 4206 319 1238 2287 3218 4219 337 1245 2289 3241 4220 342 1247 2324 3250 4227 346 1248 2327 3255 4229 349 1258 2336 3269 4231 366 1265 2340 3277 4242 367 1266 2346 3278 4256 376 1285 2357 3279 4285 380 1290 2360 3285 4288 384 1309 2369 3295 4289 405 1312 2373 3302 4297 415 1316 2378 3310 4300 422 1356 2393 3312 4303 424 1359 2405 3323 4309 432 1367 2406 3333 4313 438 1369 2409 3334 4327 451 1371 2410 3367 4337 489 1375 2416 3385 4350 496 1377 2427 3396 4352 510 1379 2428 3401 4364 514 1384 2435 3407 4376 516 1415 2438 3412 4377 517 1429 2440 3430 4380 528 1438 2450 3435 4397 543 1454 2451 3437 4415 551 1456 2455 3442 4434 562 1457 2462 3455 4436 567 1459 2471 3479 4444 571 1467 2475 3481 4445 576 1474 2486 3490 4447 577 1476 2491 3500 4461 609 1478 2508 3508 4463 614 1486 2513 3528 4466 620 1489 2520 3531 4469 629 1491 2526 3535 4473 650 1504 2563 3538 4497 652 1505 2568 3546 4503 654 1529 2587 3585 4504 661 1531 2594 3590 4505 667 1535 2595 3599 4529 668 1569 2598 3604 4533 679 1579 2602 3606 4552 695 1581 2629 3616 4571 708 1583 2631 3622 4576 711 1588 2649 3633 4584 748 1601 2656 3647 4596 764 1617 2659 3667 4601 767 1628 2664 3674 4621 768 1638 2674 3677 4631 772 1645 2680 3680 4676 781 1671 2684 3682 4677 788 1682 2692 3689 4682 811 1687 2704 3691 4715 820 1690 2721 3694 4725 823 1749 2752 3716 4727 847 1768 2757 3728 4762 874 1772 2780 3743 4768 877 1788 2795 3813 4778 890 1795 2798 3819 4781 891 1799 2822 3820 4785 910 1802 2824 3828 4787 917 1814 2826 3830 4807 934 1826 2827 3837 937 1829 2841 3838 938 1833 2842 3843 946 1840 2857 3851 948 1847 2876 3853 962 1876 2883 3858 964 1884 2886 3859 965 1901 2896 3860 966 1908 2912 3879 967 1924 2917 3893 974 1952 2919 3907 984 1956 2932 3912 986 1968 2955 3915 The One Over None
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Looks very well done, but honestly, I don't understand what the values stand for.
I have a good basic background for statistics, but this, I don't know! -
Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineQuote: Originally posted by SergeM on Mar 9, 2013
Looks very well done, but honestly, I don't understand what the values stand for.
I have a good basic background for statistics, but this, I don't know!Yep, right about that, not standard.
But this is still early in the explanation.
We're giving examples right now.
Later we'll look at Projection.
That may include what you are more use to, Statistics.
What you are looking at now are the Dynamics.The One Over None
I Know... -
Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineFor the Draw Index example, we made a change only to the Amp / Freq Ratio = 0.90
Below is the shortened table.
Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 BMA Degree 5 BMA Degree 6 BMA Degree 7 BMA Degree 8 Algorithm Predictor A = 28.31013765 -2.116 -1.24 -0.636 0.021 0.664 1.276 1.854 2.15 Wave / BMA = 0.50 Depth = 25 B = 9.734994807 Amp / Freq = 0.90 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Remainder Data 1 38.04513246 18.96078997 -2.972189035 -15.42680224 -17.95035921 -9.437734464 -4.100218602 -3.087169942 -1.744203738 -1.287245192 1 2 47.78012727 19.04301835 -2.307719562 -13.96065417 -16.18908449 -7.641138834 -1.312854994 0.164692237 0.310532255 0.113081941 26 3 57.51512207 19.13351384 -1.507855818 -12.04163353 -13.70737543 -5.46488175 1.364315725 2.912643143 1.986029889 2.810121844 53 4 67.25011688 19.2316382 -0.588967853 -9.739760489 -10.69988694 -3.65454221 1.982173032 2.033486324 0.356650928 -1.170907876 65 5 76.98511169 19.3367007 0.430360604 -7.136492352 -7.328418185 -2.328678441 1.434203359 1.152472679 -0.118765118 -1.426494937 81 6 86.7201065 19.44796371 1.529840761 -4.321433457 -3.722982844 -1.348009603 0.301089899 0.979054968 0.923893239 2.490476827 103 7 96.4551013 19.5646486 2.688026248 -1.389040073 0.008028417 -0.473621949 -1.628817981 -1.275559989 -0.610284575 -1.338480003 112 8 106.1900961 19.68594185 3.882883534 1.564801556 3.770830729 0.822767004 -2.683377635 -2.273013659 -0.75754737 -1.203382118 129 9 115.9250909 19.81100132 5.092352906 4.4455033 7.441518619 2.806066979 -2.276364232 -1.966125716 0.101693141 4.619262766 156 10 125.6600857 19.9389626 6.294898783 7.163008126 10.84888065 5.329797574 -0.950899529 -3.357091435 -3.20723633 -7.720406164 160 … … … … … … … … … … … … 478 4681.637656 10.55527845 2.574187333 3.921019886 5.017234443 3.746618763 1.096871021 0.604703355 1.272980448 4.573450754 4715 479 4691.37265 10.73141706 3.721394659 5.676338899 6.37852156 4.666473159 1.176947289 -0.364514437 0.098119732 1.542651729 4725 480 4701.107645 10.90092603 4.827223744 7.290699313 7.352140627 5.294374798 1.386985975 -2.033136566 -2.631658309 -6.495200773 4727 481 4710.84264 11.06311785 5.878844609 8.740979646 7.896702635 5.673198357 3.405398099 2.061890848 1.555169384 4.882058606 4762 482 4720.577635 11.21732433 6.864283864 10.0120566 7.980502995 5.163695409 3.816336724 2.481010272 0.970266953 -1.083111924 4768 483 4730.31263 11.36290156 7.772569468 11.09793818 7.651504561 3.705675519 2.713616793 1.909696705 0.820849302 0.65261833 4778 484 4740.047624 11.4992346 8.593814749 12.00048656 7.024397266 1.55974887 0.373580968 0.080475108 -0.030024711 -0.149337796 4781 485 4749.782619 11.62574207 9.319278644 12.72767495 6.265974109 -0.684546449 -2.005188128 -1.687290033 -0.824482604 0.480218239 4785 486 4759.517614 11.74188047 9.941391542 13.29084461 5.551150063 -2.442312448 -3.330795136 -2.480562951 -1.64057902 -3.148631133 4787 Quantum 487 4769.252609 11.84714818 10.45376363 13.70192088 5.023690587 -3.372776694 -2.90555253 -0.008360407 1.004837212 2.002720331 4807 Projection -> 488 4778.987604 11.89706587 10.6824153 13.87162544 4.821004732 -3.595017432 -2.051928387 3.044818632 4.851165316 1.131558648 4823.640312 The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineNow for our next set, we will look at my state's lottery Gopher 5.
It's a Pick 5 of 47 numbers.
Below is a shortened list of draws for the dates from 2005-09-23 to 2013-03-01.
Index Year Month Day A B C D E 1 2005 9 23 4 25 27 44 45 2 2005 9 27 1 8 15 26 38 3 2005 9 30 7 8 12 37 39 4 2005 10 4 19 30 31 32 37 5 2005 10 7 12 22 27 40 46 6 2005 10 11 20 26 29 33 45 7 2005 10 14 2 13 24 25 34 8 2005 10 18 19 20 24 41 43 9 2005 10 21 10 18 25 34 40 10 2005 10 25 16 18 25 26 31 … … … … … … … … … 998 2013 2 8 5 12 19 21 34 999 2013 2 11 13 14 18 30 38 1000 2013 2 13 1 7 34 39 45 1001 2013 2 15 11 14 24 31 44 1002 2013 2 18 20 30 35 39 45 1003 2013 2 20 3 13 17 24 45 1004 2013 2 22 2 14 17 18 27 1005 2013 2 25 27 28 35 36 38 1006 2013 2 27 6 26 38 45 47 1007 2013 3 1 10 26 32 40 41 First we will analyze the individual column C for this example.
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineWhen we play around with the controls, we find that we need only 2 waves to generalize a Wave Matrix.
Also, we tweaked the Amp / Freq to 0.60 to get the BMA values near equal and opposite.
Below is a shortened table.
Type = Linear BMA Degree 1 BMA Degree 2 Algorithm Predictor A = 24.41198094 -0.07 0.108 Wave / BMA = 0.50 Depth = 25 B = -1.70397E-06 Amp / Freq = 0.60 Level = 25 Index Regression Wave 1 Wave 2 Remainder Data 1 24.41197924 -0.832644354 -0.363340618 3.784005732 27 2 24.41197754 -0.761880194 -0.263381292 -8.386716051 15 3 24.41197583 -0.650034918 -0.071974973 -11.68996594 12 4 24.41197413 -0.512988151 0.199986122 6.9010279 31 5 24.41197242 -0.395638267 0.473378918 2.510286925 27 6 24.41197072 -0.334747727 0.692949272 4.229827734 29 7 24.41196902 -0.358412488 0.821516515 -0.875073044 24 8 24.41196731 -0.47958729 0.848155041 -0.780535064 24 9 24.41196561 -0.70456965 0.759539161 0.533064881 25 10 24.4119639 -1.032205604 0.537428157 1.082813542 25 … … … … … … 998 24.41028039 1.509340657 -0.573850577 -6.345770465 19 999 24.41027868 1.809791129 -0.501868081 -7.718201729 18 1000 24.41027698 2.153416571 -0.414440425 7.850746877 34 1001 24.41027527 2.531105339 -0.318588242 -2.622792371 24 1002 24.41027357 2.946569388 -0.183329824 7.826486867 35 1003 24.41027187 3.396405209 0.014440297 -10.82111737 17 1004 24.41027016 3.880932917 0.320690376 -11.61189346 17 1005 24.41026846 4.368250026 0.709207821 5.512273696 35 1006 24.41026675 4.795623099 1.079658238 7.71445191 38 Quantum 1007 24.41026505 5.107259674 1.348519025 1.133956252 32 Projection -> 1008 24.41026335 5.229102135 1.450337056 -6.185351446 24.90435109 The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineNext example is the Combinatorial Index.
The Combinatorial Index is the number assigned to a combination when all possible combinations are sorted with lower ball number combinations first and higher ball number combinations last.
Here's the short list of Combinatorial Index for this example of Pick 5 of 47.
Combinatorial Index A B C D E 1 1 2 3 4 5 2 1 2 3 4 6 3 1 2 3 4 7 4 1 2 3 4 8 5 1 2 3 4 9 6 1 2 3 4 10 7 1 2 3 4 11 8 1 2 3 4 12 9 1 2 3 4 13 … … … … … … 1533930 41 43 44 45 47 1533931 41 43 44 46 47 1533932 41 43 45 46 47 1533933 41 44 45 46 47 1533934 42 43 44 45 46 1533935 42 43 44 45 47 1533936 42 43 44 46 47 1533937 42 43 45 46 47 1533938 42 44 45 46 47 1533939 43 44 45 46 47 The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineWe can transform the combination to an Index and back to a combination using an Excel file we have created.
It contains some functions for going between the two.
The file can be found at http://www.jadexcode.com/files/excel/combinatorialindex.xlsor at ftp://www.jadexcode.com/excel/combinatorialindex.xls
The functions in the file that do the transformation are documented in the sheet tab labeled 'Function Information'.
The two used in the transformation are ' =Index2Combin(N, R, Index) ' and ' =Combin2Index(N, R, Range) '.
The following is a short list for this example.Index Year Month Day A B C D E Combinatorial Index 1 2005 9 23 4 25 27 44 45 562881 2 2005 9 27 1 8 15 26 38 75755 3 2005 9 30 7 8 12 37 39 787082 4 2005 10 4 19 30 31 32 37 1432604 5 2005 10 7 12 22 27 40 46 1195495 6 2005 10 11 20 26 29 33 45 1446315 7 2005 10 14 2 13 24 25 34 263789 8 2005 10 18 19 20 24 41 43 1416319 9 2005 10 21 10 18 25 34 40 1072666 10 2005 10 25 16 18 25 26 31 1338511 … … … … … … … … … … 998 2013 2 8 5 12 19 21 34 627297 999 2013 2 11 13 14 18 30 38 1210964 1000 2013 2 13 1 7 34 39 45 71479 1001 2013 2 15 11 14 24 31 44 1114133 1002 2013 2 18 20 30 35 39 45 1450579 1003 2013 2 20 3 13 17 24 45 397240 1004 2013 2 22 2 14 17 18 27 266774 1005 2013 2 25 27 28 35 36 38 1514275 1006 2013 2 27 6 26 38 45 47 778471 1007 2013 3 1 10 26 32 40 41 1091575 The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
OfflineFor this example we find that a setting of 'by Amplitude Only' and Wave to BMA RMS Ratio = 0.12 works well for 8 waves; everything else default.
Here's the short list for the Wave Matrix.Type = Linear BMA Degree 1 BMA Degree 2 BMA Degree 3 BMA Degree 4 BMA Degree 5 BMA Degree 6 BMA Degree 7 BMA Degree 8 Algorithm Predictor A = 763985.1424 -1.343 -0.456 0.111 0.551 0.901 1.141 1.311 1.451 Wave / BMA = 0.12 Depth = 25 B = 28.86518294 Amp / Freq = 0.50 Level = 25 Index Regression Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Remainder Data 1 764014.0076 -1771.446462 27473.96137 -56037.41632 -119648.8512 -112236.5224 -66898.2488 -28688.49345 -3885.466383 160559.4761 562881 2 764042.8728 -1803.844516 33866.58951 -28800.48641 -78775.0107 -82572.21369 -65708.5483 -56490.09934 -54961.00694 -353043.2524 75755 3 764071.738 -1857.436604 41717.65733 8400.847536 -13393.69143 -7600.811099 3715.427912 4023.030243 -1798.34902 -10196.41285 787082 4 764100.6032 -1948.849267 49687.95876 47154.18366 52852.44522 66044.86042 74130.68386 71129.58481 62444.35164 247008.1777 1432604 5 764129.4684 -2095.53973 56421.97002 79072.84929 96356.11667 89538.56282 72805.8758 57278.58758 44116.98464 -62129.87544 1195495 6 764158.3335 -2314.547627 60834.95339 99591.96603 114632.4906 73874.28252 28437.47828 7093.718903 3338.685275 296667.6391 1446315 7 764187.1987 -2621.691441 62202.05498 107013.8755 114350.6981 41523.56541 -34456.46096 -72371.6703 -84657.16143 -631381.4085 263789 8 764216.0639 -3030.610509 60278.54158 102802.4198 114276.5944 56846.55142 4890.156286 -9964.877681 -3658.8693 329663.0301 1416319 9 764244.9291 -3552.791856 55115.61096 85802.52193 101096.6004 66073.00315 33288.88682 22619.45364 21606.13312 -73628.34731 1072666 10 764273.7943 -4196.912167 47239.57233 57386.00816 71521.67479 57964.76068 42986.45447 36427.63065 32869.29324 232038.7236 1338511 … … … … … … … … … … … … 998 792792.595 64145.36769 -9072.479442 -14424.99605 13047.03612 25304.6393 19327.64208 10221.91674 3349.761778 -277394.4832 627297 999 792821.4602 68071.12194 -3897.451619 -14899.17798 1537.872898 5550.373109 -686.1147227 -4295.845887 -1674.953511 368436.7156 1210964 1000 792850.3254 71852.79639 1794.942106 -15858.61368 -12598.18376 -19836.66208 -35345.62339 -50412.87449 -62626.65476 -598340.4517 71479 1001 792879.1906 75457.67762 8002.224263 -14610.12772 -15029.47856 -4020.025168 12637.8558 25217.00001 31627.22984 201971.4534 1114133 1002 792908.0557 78852.09172 14555.62068 -12051.95304 -19672.66214 -5170.813148 24348.58933 49728.30566 65620.92682 461460.8384 1450579 1003 792936.9209 82002.42304 21267.07984 -8029.68596 -29652.6369 -41889.29511 -42127.08268 -40345.51575 -41129.12886 -295793.0785 397240 1004 792965.7861 84875.91332 27958.92542 -96.74586456 -25051.80152 -45686.11496 -55272.19383 -58771.92233 -60338.60371 -393809.2426 266774 1005 792994.6513 87441.13844 34295.2526 11367.77212 -3041.342769 -4773.847528 5883.563458 20302.71213 34940.02255 534865.0777 1514275 1006 793023.5165 89668.7596 39777.2129 22343.00329 15581.99863 14762.51111 13260.26995 9310.634623 4500.056745 -223756.9633 778471 Quantum 1007 793052.3817 91532.78192 43997.10068 30996.83365 30498.58013 31413.32791 24449.76756 13609.02915 4572.92674 27452.27059 1091575 Projection -> 1008 793081.2468 92373.00748 45764.92381 34875.33076 39732.42611 51740.4845 60277.58958 67069.28709 78015.94715 341172.745 1604102.988 The One Over None
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Native American Eagle SunMN
United States
Member #21
December 7, 2001
4,812 Posts
Offlinetime for eats.
back in a while.
The One Over None
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